Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "124" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459998 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 9.143567 | 0.120104 | 9.743539 | 0.748491 | 7.726665 | 0.877840 | 0.492488 | 0.409210 | 0.0393 | 0.6506 | 0.4578 | nan | nan |
| 2459997 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.031027 | 0.140480 | 10.321598 | 0.904913 | 7.555283 | 0.699998 | 1.366643 | 0.882506 | 0.0429 | 0.6624 | 0.4595 | nan | nan |
| 2459996 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.136365 | 0.460256 | 12.935840 | 1.250021 | 7.156589 | 1.036588 | 0.308290 | 1.003557 | 0.0413 | 0.6628 | 0.4623 | nan | nan |
| 2459995 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 11.395834 | 0.131974 | 12.046160 | 0.845472 | 7.774116 | 0.620999 | 0.163523 | 1.027516 | 0.0467 | 0.6540 | 0.4702 | nan | nan |
| 2459994 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.897811 | 0.118799 | 10.409491 | 0.934326 | 7.616419 | 0.633014 | 0.070847 | 0.328494 | 0.0411 | 0.6480 | 0.4688 | nan | nan |
| 2459993 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.051542 | -0.386895 | 9.692039 | 0.766643 | 9.972744 | -0.352565 | 0.601242 | 0.454526 | 0.0353 | 0.6540 | 0.4527 | nan | nan |
| 2459991 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.938419 | 0.003311 | 10.266880 | 0.795340 | 8.999647 | 0.353539 | 0.084600 | 0.563367 | 0.0393 | 0.6594 | 0.4638 | nan | nan |
| 2459990 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.463037 | 0.029839 | 10.059791 | 0.710930 | 8.896452 | 0.280833 | -0.094851 | 0.277059 | 0.0428 | 0.6584 | 0.4640 | nan | nan |
| 2459989 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 10.225287 | 0.024409 | 8.953567 | 0.865866 | 7.853169 | 0.480250 | -0.071782 | 0.472155 | 0.0390 | 0.6539 | 0.4635 | nan | nan |
| 2459988 | digital_ok | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 12.273076 | 0.094583 | 10.376144 | 0.637213 | 10.593768 | 0.027037 | -0.026777 | 0.185159 | 0.0384 | 0.6567 | 0.4632 | nan | nan |
| 2459987 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.344579 | 0.095908 | 0.207524 | 0.816860 | -0.291051 | 0.470652 | 0.925632 | 0.416557 | 0.6512 | 0.6603 | 0.3664 | nan | nan |
| 2459986 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.041138 | 0.381264 | 0.225876 | 0.742746 | -0.403863 | 0.234431 | 0.443873 | 0.327300 | 0.6715 | 0.6844 | 0.3210 | nan | nan |
| 2459985 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.224629 | 0.458750 | 0.267167 | 0.786922 | -0.706505 | 0.348457 | 1.867668 | 1.253428 | 0.6506 | 0.6580 | 0.3747 | nan | nan |
| 2459984 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.433938 | 0.635109 | 0.281079 | 0.874114 | 0.039125 | 0.078375 | 0.397759 | 0.357201 | 0.6659 | 0.6673 | 0.3473 | nan | nan |
| 2459983 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.187504 | 0.043151 | 0.192195 | 0.673965 | -0.737491 | 0.271295 | 0.357921 | 0.124349 | 0.6783 | 0.6990 | 0.3111 | nan | nan |
| 2459982 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.015350 | 0.646756 | 0.337886 | 0.827693 | 0.018308 | 0.530108 | 0.187032 | 0.656958 | 0.7315 | 0.7338 | 0.2664 | nan | nan |
| 2459981 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.030160 | -0.144157 | 0.042204 | 0.610690 | -0.848452 | 0.036622 | 1.184209 | 0.628071 | 0.6507 | 0.6629 | 0.3699 | nan | nan |
| 2459980 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.034469 | -0.199903 | -0.018160 | 0.655968 | -0.580273 | 0.436318 | 0.229166 | 0.739589 | 0.6954 | 0.7063 | 0.2901 | nan | nan |
| 2459979 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.031740 | -0.196408 | -0.186615 | 0.577990 | -0.609087 | 0.182369 | 1.125081 | 0.812557 | 0.6441 | 0.6600 | 0.3721 | nan | nan |
| 2459978 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.107480 | -0.170832 | -0.135642 | 0.572970 | -0.549593 | -0.003467 | 0.607166 | 0.155058 | 0.6446 | 0.6590 | 0.3776 | nan | nan |
| 2459977 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.188084 | 0.001042 | -0.070741 | 0.599728 | 0.240803 | 0.360207 | 0.565734 | 0.125996 | 0.6116 | 0.6280 | 0.3450 | nan | nan |
| 2459976 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.066287 | 0.017979 | -0.051088 | 0.601790 | -0.698799 | 0.097561 | 0.350440 | 0.183752 | 0.6513 | 0.6640 | 0.3660 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Power | 9.743539 | 9.143567 | 0.120104 | 9.743539 | 0.748491 | 7.726665 | 0.877840 | 0.492488 | 0.409210 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Power | 10.321598 | 10.031027 | 0.140480 | 10.321598 | 0.904913 | 7.555283 | 0.699998 | 1.366643 | 0.882506 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Power | 12.935840 | 11.136365 | 0.460256 | 12.935840 | 1.250021 | 7.156589 | 1.036588 | 0.308290 | 1.003557 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Power | 12.046160 | 11.395834 | 0.131974 | 12.046160 | 0.845472 | 7.774116 | 0.620999 | 0.163523 | 1.027516 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Shape | 10.897811 | 10.897811 | 0.118799 | 10.409491 | 0.934326 | 7.616419 | 0.633014 | 0.070847 | 0.328494 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Shape | 12.051542 | 12.051542 | -0.386895 | 9.692039 | 0.766643 | 9.972744 | -0.352565 | 0.601242 | 0.454526 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Shape | 12.938419 | 12.938419 | 0.003311 | 10.266880 | 0.795340 | 8.999647 | 0.353539 | 0.084600 | 0.563367 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Shape | 10.463037 | 0.029839 | 10.463037 | 0.710930 | 10.059791 | 0.280833 | 8.896452 | 0.277059 | -0.094851 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Shape | 10.225287 | 0.024409 | 10.225287 | 0.865866 | 8.953567 | 0.480250 | 7.853169 | 0.472155 | -0.071782 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Shape | 12.273076 | 0.094583 | 12.273076 | 0.637213 | 10.376144 | 0.027037 | 10.593768 | 0.185159 | -0.026777 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Discontinuties | 0.925632 | -0.344579 | 0.095908 | 0.207524 | 0.816860 | -0.291051 | 0.470652 | 0.925632 | 0.416557 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 0.742746 | 0.381264 | 0.041138 | 0.742746 | 0.225876 | 0.234431 | -0.403863 | 0.327300 | 0.443873 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Discontinuties | 1.867668 | 0.458750 | 0.224629 | 0.786922 | 0.267167 | 0.348457 | -0.706505 | 1.253428 | 1.867668 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 0.874114 | -0.433938 | 0.635109 | 0.281079 | 0.874114 | 0.039125 | 0.078375 | 0.397759 | 0.357201 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 0.673965 | -0.187504 | 0.043151 | 0.192195 | 0.673965 | -0.737491 | 0.271295 | 0.357921 | 0.124349 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 0.827693 | -0.015350 | 0.646756 | 0.337886 | 0.827693 | 0.018308 | 0.530108 | 0.187032 | 0.656958 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Discontinuties | 1.184209 | -0.144157 | 0.030160 | 0.610690 | 0.042204 | 0.036622 | -0.848452 | 0.628071 | 1.184209 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Temporal Discontinuties | 0.739589 | -0.199903 | 0.034469 | 0.655968 | -0.018160 | 0.436318 | -0.580273 | 0.739589 | 0.229166 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Discontinuties | 1.125081 | 0.031740 | -0.196408 | -0.186615 | 0.577990 | -0.609087 | 0.182369 | 1.125081 | 0.812557 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | ee Temporal Discontinuties | 0.607166 | -0.170832 | 0.107480 | 0.572970 | -0.135642 | -0.003467 | -0.549593 | 0.155058 | 0.607166 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 0.599728 | 0.188084 | 0.001042 | -0.070741 | 0.599728 | 0.240803 | 0.360207 | 0.565734 | 0.125996 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | N09 | digital_ok | nn Power | 0.601790 | 0.017979 | 0.066287 | 0.601790 | -0.051088 | 0.097561 | -0.698799 | 0.183752 | 0.350440 |